Advertisement

On Global Minimization in Mathematical Modelling of Engineering Applications

  • Raimondas Čiegis
Part of the Optimization and Its Applications book series (SOIA, volume 4)

Abstract

Many problems in engineering, physics, economic and other subjects may be formulated as optimization problems, where the minimum value of an objective function should be found. Mathematically the problem is formulated as follows
$$ f* = \mathop {\min }\limits_{X \in D} f(X), $$
(1)
where f(X) is an objective function, X are decision variables, and D is a search space. Besides of the minimum f*, one or all minimizers X* : f (X*) = f* should be found.

Keywords

Trial Point Local Optimization Method Separate Beam Pile Raft Foundation Global Minimum Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [BBC02]
    Baravykaitė, M., Belevičius, R., Čiegis, R.: One application of the parallelization tool of Master-Slave algorithms. Informatica, 13(4), 393–404 (2002)Google Scholar
  2. [BCZ05]
    Baravykaitė, M., Čiegis, R., Žilinskas, J.: Template realization of generalized Branch and Bound algorithm. Matematical Modelling and Analysis, 10, 217–236 (2005)Google Scholar
  3. [BIS99]
    Baronas, R., Ivanauskas, F., Sapagovas, M.: Modelling of wood drying and an influence of lumber geometry on drying dynamics. Nonlinear Analysis: Modelling and Control, 4, 11–21 (1999)zbMATHGoogle Scholar
  4. [BVM02]
    Belevičius, R., Valentinavičius, S., Michnevič, E.: Multilevel optimization of grillages. Journal of Civil Engineering and Management, 8(1), 98–103 (2002)Google Scholar
  5. [CBB04]
    Čiegis, R., Baravykaitė, M., Belevičius, R.: Parallel global optimization of foundation schemes in civil engineering. In: Dongarra, J., Madsen, K, Wasniewski, J. (eds) PARA04, Workshop on State of the Art in Scientific Computing. Lecture Notes in Computer Science, Vol. 3732, Springer, 305–312 (2005)Google Scholar
  6. [CS02]
    Čiegis, R., Starikovičius, V.: Mathematical modeling of wood drying process. Mathematical Modelling and Analysis, 7(2), 177–190 (2002)MathSciNetGoogle Scholar
  7. [CSS04]
    Čiegis, R., Starikovičius, V., Štikonas, A.: Parameters identification algorithms for wood drying modeling. In: Buikis, A., Čiegis, R., Fitt, A.D. (eds) Progress in Industrial Mathematics at ECMI2002. Mathematics in Industry-ECMI Subseries, Vol. 5, Springer, Berlin Heidelberg New York, 107–112 (2004)Google Scholar
  8. [KLCL04]
    Kim, K., Lee, S., Chung, C, Lee, H.: Optimal pile placement for minimizing differential settlements in piled raft foundations. http://strana.snu.ac.kr/laboratory/publications (2004)Google Scholar
  9. [Ped89]
    Pedersen, P.: Design for minimum stress concentration — some practical aspects. In: Structural Optimization. Kluwer Academic, 225–232 (1989)Google Scholar
  10. [PM95]
    Perre, P., Mosnier, S.: Vacuum drying with radiative heating. Vacuum Drying of Wood, 95, (1995)Google Scholar
  11. [PSO85]
    Plumb, O., Spolek, G., Olmstead, B.: Heat and mass transfer in wood during drying. Int. J. Heat Mass Transfer, 28(9), 1669–1678 (1985)CrossRefGoogle Scholar
  12. [SL97]
    Simpson, W.T., Liu, J.T.: An optimization technique to determine red oak surface and internal moisture transfer coefficients during drying. Wood Fiber Sci., 29(4), 312–318 (1997)Google Scholar
  13. [TA86]
    Tichonov, A., Arsenin, V.: Methods for Solution of Ill-posed Problems. Nauka, Moscow (1986)Google Scholar
  14. [Zil01]
    Žilinskas, J.: Black box global optimization inspired by interval methods. Information Technology and Control, 21(4), 53–60 (2001)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Raimondas Čiegis
    • 1
  1. 1.Vilnius Gediminas Technical UniversityVilniusLithuania

Personalised recommendations